Students’ perceptions of assessment: a comparative analysis between Portugal and Sweden

This paper aims at investigating students’ perceptions about assessment, especially the ways in which it is put into practice. Data were collected through questionnaires in different programmes in Portugal and Sweden. In total, 173 students from Portugal and 72 from Sweden participated in the study. Findings showed that students had similar ideas about assessment, such as verification of knowledge and learning, tests and grades. Their experiences of assessment methods used varied in the two countries, which can partly be explained by differences in national education systems. A learner-oriented perspective is prominent in the use of assessment methods, but at the same time student influence on assessment is perceived as low in both countries. Implications of the findings are analysed, namely issues regarding a learner-oriented perspective and the effectiveness, influence, trust, times and methods of assessment.Understanding the assessment process, including the concepts and methods used, is essential to educational practice. In recent years new trends on assessment have emerged from an integrated perspective of the teaching, learning and assessment process (Rust 2007). In contrast to summative assessment, which can be perceived as mainly using assessment to certify student achievement (Boud and Falchikov 2006; Hernández 2012), formative assessment supports and monitors the students’ learning, providing continuous feedback during the process (Yorke 2003; Weurlander et al. 2012), and informing them about their performance (Boud 1990; Brown and Knight 1994; Brew et al. 2009). These trends have introduced new methods of assessment (Brew et al. 2009) and more participatory practices, such as self, peer, and co-assessment (Dochy, Segers and Sluijsmans 1999).National Funds through the FCT (Foundation for Science and Technology) and co-financed by European Regional Development Funds (FEDER) through the Competitiveness and Internationalization Operational Program (POCI) through CIEC (Research Centre on Child Studies, of the University of Minho) with the reference POCI-01-0145-FEDER-007562Strategic Project UID/CED/00317/2013CIEC – Research Centre on Child Studies, IE, UMinho (FCT R&D unit 317), Portugalinfo:eu-repo/semantics/publishedVersio

The gathering firestorm in southern Amazonia.

Wildfires, exacerbated by extreme weather events and land use, threaten to change the Amazon from a net carbon sink to a net carbon source. Here, we develop and apply a coupled ecosystem-fire model to quantify how greenhouse gas-driven drying and warming would affect wildfires and associated CO2 emissions in the southern Brazilian Amazon. Regional climate projections suggest that Amazon fire regimes will intensify under both low- and high-emission scenarios. Our results indicate that projected climatic changes will double the area burned by wildfires, affecting up to 16% of the region's forests by 2050. Although these fires could emit as much as 17.0 Pg of CO2 equivalent to the atmosphere, avoiding new deforestation could cut total net fire emissions in half and help prevent fires from escaping into protected areas and indigenous lands. Aggressive efforts to eliminate ignition sources and suppress wildfires will be critical to conserve southern Amazon forests

The (mis)use of graphs insights into the portuguese companies´ annual reports

Graphs area suitable format for summarizing and disclosinginformation in annual reports given thatinvestors,and other addressees of graphs, may lack of the time required to fully analysethe information. Therefore, graphs should be reliable,accurateand free from material distortions. This Work Project aims to make aware of the importance that graphs have both for the report’s usersand the companies themselves. Moreover, this project investigates the potential roots of graphical distortions. The findings suggestthatthe correlation between the level of graph distortion in Portugal and the Board of Directors ismoderate, although not significant

Neonatal Skull Depression: The Role of Cranial Ultrasound

Nontraumatic congenital neonatal skull depression is a rare condition resulting from external forces shaping the fetal skull. Typically, newborns are asymptomatic, and, usually, the condition resolves in a few months with no need for intervention. However, many newborns undergo a CT scan, an ionizing technique, to check for fractures or intracranial lesions. We report a case of congenital skull depression without neurological deficits, managed conservatively through clinical monitoring and ultrasound.info:eu-repo/semantics/publishedVersio

Cloudbus Toolkit for Market-Oriented Cloud Computing

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape

Neonatal Skull Depression: The Role of Cranial Ultrasound

Nontraumatic congenital neonatal skull depression is a rare condition resulting from external forces shaping the fetal skull. Typically, newborns are asymptomatic, and, usually, the condition resolves in a few months with no need for intervention. However, many newborns undergo a CT scan, an ionizing technique, to check for fractures or intracranial lesions. We report a case of congenital skull depression without neurological deficits, managed conservatively through clinical monitoring and ultrasound.info:eu-repo/semantics/publishedVersio

Automated Weed Detection Systems: A Review

A weed plant can be described as a plant that is unwanted at a specific location at a given time. Farmers have fought against the weed populations for as long as land has been used for food production. In conventional agriculture this weed control contributes a considerable amount to the overall cost of the produce. Automatic weed detection is one of the viable solutions for efficient reduction or exclusion of chemicals in crop production. Research studies have been focusing and combining modern approaches and proposed techniques which automatically analyze and evaluate segmented weed images. This study discusses and compares the weed control methods and gives special attention in describing the current research in automating the weed detection and control. Keywords: Detection, Weed, Agriculture 4.0, Computational vision, Robotic

Methods for Detecting and Classifying Weeds, Diseases and Fruits Using AI to Improve the Sustainability of Agricultural Crops: A Review

The rapid growth of the world’s population has put significant pressure on agriculture to meet the increasing demand for food. In this context, agriculture faces multiple challenges, one of which is weed management. While herbicides have traditionally been used to control weed growth, their excessive and random use can lead to environmental pollution and herbicide resistance. To address these challenges, in the agricultural industry, deep learning models have become a possible tool for decision-making by using massive amounts of information collected from smart farm sensors. However, agriculture’s varied environments pose a challenge to testing and adopting new technology effectively. This study reviews recent advances in deep learning models and methods for detecting and classifying weeds to improve the sustainability of agricultural crops. The study compares performance metrics such as recall, accuracy, F1-Score, and precision, and highlights the adoption of novel techniques, such as attention mechanisms, single-stage detection models, and new lightweight models, which can enhance the model’s performance. The use of deep learning methods in weed detection and classification has shown great potential in improving crop yields and reducing adverse environmental impacts of agriculture. The reduction in herbicide use can prevent pollution of water, food, land, and the ecosystem and avoid the resistance of weeds to chemicals. This can help mitigate and adapt to climate change by minimizing agriculture’s environmental impact and improving the sustainability of the agricultural sector. In addition to discussing recent advances, this study also highlights the challenges faced in adopting new technology in agriculture and proposes novel techniques to enhance the performance of deep learning models. The study provides valuable insights into the latest advances and challenges in process systems engineering and technology for agricultural activities